Seminars in respiratory and critical care medicine
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The diffusion of electronic health records collecting large amount of clinical, monitoring, and laboratory data produced by intensive care units (ICUs) is the natural terrain for the application of artificial intelligence (AI). AI has a broad definition, encompassing computer vision, natural language processing, and machine learning, with the latter being more commonly employed in the ICUs. Machine learning may be divided in supervised learning models (i.e., support vector machine [SVM] and random forest), unsupervised models (i.e., neural networks [NN]), and reinforcement learning. ⋯ Accordingly, the ICU team will benefit from models with high accuracy that will be used for both research purposes and clinical practice. These models will be also the foundation of future decision support system (DSS), which will help the ICU team to visualize and analyze huge amounts of information. We plea for the creation of a standardization of a core group of data between different electronic health record systems, using a common dictionary for data labeling, which could greatly simplify sharing and merging of data from different centers.
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Critical care clinicians strive to reverse the disease process and are frequently faced with difficult end-of-life (EoL) situations, which include transitions from curative to palliative care, avoidance of disproportionate care, withholding or withdrawing therapy, responding to advance treatment directives, as well as requests for assistance in dying. This article presents a summary of the most common issues encountered by intensivists caring for patients around the end of their life. Topics explored are the practices around limitations of life-sustaining treatment, with specific mention to the thorny subject of assisted dying and euthanasia, as well as the difficulties encountered regarding the adoption of advance care directives in clinical practice and the importance of integrating palliative care in the everyday practice of critical-care physicians. The aim of this article is to enhance understanding around the complexity of EoL decisions, highlight the intricate cultural, religious, and social dimensions around death and dying, and identify areas of potential improvement for individual practice.